Indexing Strategies for Graph Retrieval: The Entrance Points

A graph traversal is fast once you are in the graph. But how do you find the first node? If your graph contains 10 million :Person nodes, and you are looking for "Sudeep," the database shouldn't have to check every single one. This is the role of the Index.

In this lesson, we will explore the three "Gates" into your graph: Point Indices, Full-Text Indices, and the modern Vector Index. We will learn how to design "Composite Indices" for faster lookups and why an uncompressed index is the hidden cause of many Graph RAG latency failures.

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1. Point Indices (B-Tree): The Precision Strike

Goal: Find an exact match for a property (e.g., id, email, unique_name).

This is the standard index you find in SQL or MongoDB. It is extremely efficient. If you have the EmployeeID, a Point Index will land you on the node in $O(\log n)$ time.

RAG Tip: Always create a Unique Constraint on your node IDs. This automatically creates a Point Index and prevents duplicate nodes from being created during ingestion.

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2. Full-Text Indices: Handling Natural Language

Goal: Find nodes when the user provides a "Keyword" that might appear in a name or description.

If the user asks about "the Tesla project," and your node is named "Project-Tesla-2024," a Point Index will fail. A Full-Text Index (like Lucene) allows for fuzzy matching, prefix matching, and "Sounds-like" matching.

RAG Workflow:

1. User Query -> "Find anything about space."
2. Full-Text Index returns nodes: [Saturn, Apollo-11, Starship].
3. The AI agent picks the most relevant one and starts the traversal.

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3. Vector Indices: The "Semantic Gate"

Goal: Find nodes based on mathematical similarity (The "Vector RAG" bridge).

Modern Graph databases (like Neo4j 5.x) allow you to store a Vector Embedding directly on a node and index it using HNSW (Hierarchical Navigable Small World).

The Hybrid Power:

• You can search for "Nodes that sound like the user's question."
• Once you find the top 3 similar nodes, you switch to Graph Traversal.

graph TD
    Q[User Query] -->|Exact Match| PI[Point Index]
    Q -->|Fuzzy Keywords| FI[Full-Text Index]
    Q -->|Semantic Vibe| VI[Vector Index]
    
    PI --> N[Initial Node]
    FI --> N
    VI --> N
    
    N -->|Start Walk| KG[Graph Traversal]
    
    style N fill:#4285F4,color:#fff
    style KG fill:#34A853,color:#fff

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4. Implementation: Creating Indices in Cypher

Let's look at the commands to set up our "Gates."

// 1. Point Index (Unique Constraint)
CREATE CONSTRAINT person_id_unique IF NOT EXISTS
FOR (p:Person) REQUIRE p.id IS UNIQUE;

// 2. Full-Text Index (For Names)
CREATE FULLTEXT INDEX person_name_search IF NOT EXISTS
FOR (p:Person) ON EACH [p.name];

// 3. Vector Index (For Descriptions)
CREATE VECTOR INDEX project_desc_vector IF NOT EXISTS
FOR (p:Project) ON (p.embedding)
OPTIONS {indexConfig: {
  `vector.dimensions`: 1536,
  `vector.similarity_function`: 'cosine'
}};

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5. Summary and Exercises

Indexing is the "Zero Hop" of your retrieval.

• Point Indices are for exact IDs and speed.
• Full-Text Indices are for natural language keywords.
• Vector Indices bridge the gap between "Vague Meanings" and "Specific Nodes."
• Unique Constraints are your best friend for data integrity.

Exercises

1. Index Selection: You are looking for a car by its "License Plate." Which index type (Point, Full-Text, or Vector) is the most appropriate?
2. Hybrid Gate: A user asks: "Who is the CEO of that fruit company?". How would you use a Vector Index to find the Apple node and a Graph Traversal to find the CEO node?
3. Performance Check: If you add a Full-Text index to a 100-character property, how much "Extra Space" (Estimation) does it take compared to the raw text? (Hint: Indices can sometimes be larger than the data itself).

In the next lesson, we will look at making the traversal itself faster: Performance Tuning and Query Optimization.